Package: TSPred 5.1.1

Rebecca Pontes Salles

TSPred: Functions for Benchmarking Time Series Prediction

Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Authors:Rebecca Pontes Salles [aut, cre, cph], Eduardo Ogasawara [ths]

TSPred_5.1.1.tar.gz
TSPred_5.1.1.zip(r-4.7-any)TSPred_5.1.1.zip(r-4.6-any)TSPred_5.1.1.zip(r-4.5-any)
TSPred_5.1.1.tgz(r-4.6-any)TSPred_5.1.1.tgz(r-4.5-any)
TSPred_5.1.1.tar.gz(r-4.7-any)TSPred_5.1.1.tar.gz(r-4.6-any)
TSPred_5.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
TSPred/json (API)

# Install 'TSPred' in R:

install.packages('TSPred', repos = c('https://rebeccasalles.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rebeccasalles/tspred/issues

Datasets:

CATS - Time series of the CATS Competition
CATS.cont - Continuation dataset of the time series of the CATS Competition
EUNITE.Loads - Electrical loads of the EUNITE Competition
EUNITE.Loads.cont - Continuation dataset of the electrical loads of the EUNITE Competition
EUNITE.Reg - Electrical loads regressors of the EUNITE Competition
EUNITE.Reg.cont - Continuation dataset of the electrical loads regressors of the EUNITE Competition
EUNITE.Temp - Temperatures of the EUNITE Competition
EUNITE.Temp.cont - Continuation dataset of the temperatures of the EUNITE Competition
ipeadata_d - The Ipea Most Requested Dataset
ipeadata_d.cont - The Ipea Most Requested Dataset
ipeadata_m - The Ipea Most Requested Dataset
ipeadata_m.cont - The Ipea Most Requested Dataset
NN3.A - Dataset A of the NN3 Competition
NN3.A.cont - Continuation dataset of the Dataset A of the NN3 Competition
NN5.A - Dataset A of the NN5 Competition
NN5.A.cont - Continuation dataset of the Dataset A of the NN5 Competition
SantaFe.A - Time series A of the Santa Fe Time Series Competition
SantaFe.A.cont - Continuation dataset of the time series A of the Santa Fe Time Series Competition
SantaFe.D - Time series D of the Santa Fe Time Series Competition
SantaFe.D.cont - Continuation dataset of the time series D of the Santa Fe Time Series Competition

On CRAN:

benchmarking linear-models machine-learning nonstationarity time-series-forecast time-series-prediction

5.56 score 27 stars 1 packages 90 scripts 534 downloads 1 mentions 97 exports 74 dependencies

Last updated from:75d706b876. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	143
source / vignettes	OK	339
linux-release	OK	146
macos-release	OK	194
macos-oldrel	OK	195
windows-devel	OK	107
windows-release	OK	97
windows-oldrel	OK	103
wasm-release	OK	97

Exports:%>%AIC_eval AICc_eval an AN an.rev ARIMA arimainterp arimapar arimaparameters arimapred BCT BCT.rev benchmark BIC_eval BoxCoxT detrend detrend.rev DIF DIF.rev Diff DIFF Diff.rev ELM emd EMD emd.rev error ETS evaluate evaluating fitness fittestArima fittestArimaKF fittestEMD fittestLM fittestMAS fittestPolyR fittestPolyRKF fittestWavelet HW linear LogLik_eval LogT LogT.rev LT MAPE MAPE_eval marimapar marimapred mas MAS mas.rev MAXError MAXError_eval minmax MinMax minmax.rev MLM mlm_io MLP modeling MSE MSE_eval NAS NMSE NMSE_eval NNET outliers_bp pct PCT pct.rev plotarimapred postprocess preprocess processing RBF RFrst RMSE_eval slidingWindows sMAPE sMAPE_eval subset subsetting SVM sw SW Tensor_CNN Tensor_LSTM TF train train_test_subset tspred WaveletT WaveletT.rev workflow WT

Dependencies:backports base64enc class cli colorspace config cpp11 data.table dplyr e1071 elmNNRcpp farver forecast fracdiff generics ggplot2 glue gtable here insight isoband jsonlite keras KernelKnn KFAS labeling lattice lifecycle lmtest magrittr Matrix ModelMetrics MuMIn nlme nnet pillar pkgconfig plyr png processx proxy ps R6 randomForest rappdirs RColorBrewer Rcpp RcppArmadillo RcppTOML reticulate rlang Rlibeemd rprojroot RSNNS rstudioapi S7 scales tensorflow tfautograph tfdatasets tfruns tibble tidyselect timeDate urca utf8 vctrs viridisLite wavelets whisker withr yaml zeallot zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Functions for Benchmarking Time Series Prediction	TSPred-package
Adaptive Normalization	an an.rev
Time series prediction models	ARIMA ELM ETS HW MLP NNET RBF RFrst SVM Tensor_CNN Tensor_LSTM TF
Interpolation of unknown values using automatic ARIMA fitting and prediction	arimainterp
Get ARIMA model parameters	arimaparameters
Automatic ARIMA fitting and prediction	arimapred
Box Cox Transformation	BCT BCT.rev
Benchmarking a time series prediction process	benchmark benchmark.tspred
Time series of the CATS Competition	CATS
Continuation dataset of the time series of the CATS Competition	CATS.cont
Detrending Transformation	detrend detrend.rev
Differencing Transformation	Diff Diff.rev
Automatic empirical mode decomposition	emd emd.rev
Electrical loads of the EUNITE Competition	EUNITE.Loads
Continuation dataset of the electrical loads of the EUNITE Competition	EUNITE.Loads.cont
Electrical loads regressors of the EUNITE Competition	EUNITE.Reg
Continuation dataset of the electrical loads regressors of the EUNITE Competition	EUNITE.Reg.cont
Temperatures of the EUNITE Competition	EUNITE.Temp
Continuation dataset of the temperatures of the EUNITE Competition	EUNITE.Temp.cont
Evaluating prediction/modeling quality	evaluate evaluate.error evaluate.evaluating evaluate.fitness
Evaluate method for 'tspred' objects	evaluate.tspred
Prediction/modeling quality evaluation	error evaluating fitness
Automatic ARIMA fitting, prediction and accuracy evaluation	fittestArima
Automatic ARIMA fitting and prediction with Kalman filter	fittestArimaKF
Automatic prediction with empirical mode decomposition	fittestEMD
Automatically finding fittest linear model for prediction	fittestLM
Automatic prediction with moving average smoothing	fittestMAS
Automatic fitting and prediction of polynomial regression	fittestPolyR
Automatic fitting and prediction of polynomial regression with Kalman filter	fittestPolyRKF
Automatic prediction with wavelet transform	fittestWavelet
The Ipea Most Requested Dataset (daily)	ipeadata_d ipeadata_d.cont
The Ipea Most Requested Dataset (monthly)	ipeadata_m ipeadata_m.cont
Logarithmic Transformation	LogT LogT.rev
Time series transformation methods	AN BoxCoxT DIFF EMD LT MAS MinMax NAS PCT subsetting SW WT
MAPE error of prediction	MAPE
Get parameters of multiple ARIMA models.	marimapar
Multiple time series automatic ARIMA fitting and prediction	marimapred
Moving average smoothing	mas mas.rev
Maximal error of prediction	MAXError
Minmax Data Normalization	minmax minmax.rev
Time series modeling and prediction	linear MLM modeling
MSE error of prediction	MSE
Prediction/modeling quality metrics	AICc_eval AIC_eval BIC_eval LogLik_eval MAPE_eval MAXError_eval MSE_eval NMSE_eval RMSE_eval sMAPE_eval
NMSE error of prediction	NMSE
Dataset A of the NN3 Competition	NN3.A
Continuation dataset of the Dataset A of the NN3 Competition	NN3.A.cont
Dataset A of the NN5 Competition	NN5.A
Continuation dataset of the Dataset A of the NN5 Competition	NN5.A.cont
Outlier removal from sliding windows of data	outliers_bp
Percentage Change Transformation	pct pct.rev
Plot ARIMA predictions against actual values	plotarimapred
Postprocess method for 'tspred' objects	postprocess.tspred
Predict method for 'modeling' objects	predict predict.linear predict.MLM
Predict method for 'tspred' objects	predict.tspred
Preprocessing/Postprocessing time series data	postprocess postprocess.processing preprocess preprocess.processing
Preprocess method for 'tspred' objects	preprocess.tspred
Time series data processing	processing
Time series A of the Santa Fe Time Series Competition	SantaFe.A
Continuation dataset of the time series A of the Santa Fe Time Series Competition	SantaFe.A.cont
Time series D of the Santa Fe Time Series Competition	SantaFe.D
Continuation dataset of the time series D of the Santa Fe Time Series Competition	SantaFe.D.cont
sMAPE error of prediction	sMAPE
Subsetting data into training and testing sets	subset subset.tspred
Generating sliding windows of data	sw
Training a time series model	train train.linear train.MLM
Get training and testing subsets of data	train_test_subset
Train method for 'tspred' objects	train.tspred
Time series prediction process	tspred
Automatic wavelet transform	WaveletT WaveletT.rev
Executing a time series prediction process	workflow workflow.tspred